Photoelectric System Research Articles

Computational-Guided Design of Photoelectrode Active Materials for Light-Assisted Energy Storage.

The design of a novel photoelectric integrated system is considered to be an efficient way to utilize and store inexhaustible solar energy. However, the mechanism of photoelectrode under illuminate conditions is still unclear. Density functional theory (DFT) provides standardized analysis and becomes a powerful way to explain the photoelectrochemical mechanism. Herein, the feasibility of four metal oxide configurations as photoelectrode materials by using a high throughput calculation method based on DFT are investigated. According to the photoelectrochemical properties, band structure and density of states are calculated, and the intercalate/deintercalate simulation is performed with adsorption configuration. The calculation indicates that the band gap of Fe2 CoO4 (2.404eV) is narrower than that of Co3 O4 (2.553eV), as well as stronger adsorption energy (-3.293eV). The relationship between the electronic structure and the photoelectrochemical performance is analyzed and verified according to the predicted DFT results by subsequent experiments. Results show that the Fe2 CoO4 photoelectrode samples exhibit higher coulombic efficiency (97.4%) than that under dark conditions (94.9%), which is consistent with the DFT results. This work provides a general method for the design of integrated photoelectrode materials and is expected to be enlightening for the adjustment of light-assisted properties of multifunctional materials.

Fabrication of tungsten-based optical diffuser using fiberform nanostructure via efficient plasma route.

Optical diffusion is an essential process used to manage photons in a wide range of photoelectric systems. This work proposes an approach to fabricate novel optical diffusers by a plasma-processing technique, using fiberform nanostructures formed by helium plasma irradiation and subsequent annealing. After an annealing procedure in the air for oxidation, the optical properties and the light-diffusing abilities of these nanostructured thin films were studied. In addition to the morphology analysis and total transmittance measurement, the diffusion efficiency of the optical diffusers was analyzed using a transmitted scatter distribution function (TDF). It was revealed that the diffusion efficiency of a device with an irradiation time of 30 minutes could reach 97%. The results demonstrate the potential of these nanostructured optical diffusers for various photoelectric applications.

Near-real-time estimation of global horizontal irradiance from Himawari-8 satellite data

Accurate estimation of global horizontal irradiance (GHI) is not only an essential requirement of setting up a photoelectric power generation system but also critical information for terrestrial ecological models. Current methods to estimate GHI are mostly focused on hourly or daily scales and very often also require additional meteorological data. In comparison, few works have ever estimated GHI on the near-real-time scale, as the dynamically changing clouds pose great challenges for instantaneously estimating it. In this study, we adopt the Himawari-8 satellite data as the sole input without any supplementary meteorological parameters, to estimate the near-real-time GHI based on four machine learning algorithms and their ensemble. All models achieved similarly good performance, with R2 being about 0.81, and nRMSE being within the range of 25.22%–26.34%. Ground validations revealed that our result outperform the official Himawari-8 shortwave radiance product. Further analyses revealed that different machine learning models behave differently under different weather conditions, while all performed badly under overcast conditions, suggesting an in-depth investigation is required to improve the model performance. Even so, we foresee that this efficient way, which relies solely on the Himawari-8 geostationary satellite data, can be applied widely to estimate near-real-time GHI in the future.

Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network

So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.

Charge Couple Device (CCD) Photoelectric Signal Data Acquisition and Its Application in the Machine Vision of Artificial Intelligence

The development of Charge Couple Device (CCD) technology is particularly rapid in the fields of image sensors and non-contact measurement. In this study, a data acquisition device applied to CCD photoelectric detection system is designed. Among them, the design of the Differential Amplification (DA) module, Analog-to-Digital Converter (ADC) module, First In First Out (FIFO) cache module, and Complex Programmable Logic Device (CPLD) module in this device are emphasized. The ADC circuit in the ADC module converts two 4 MHz analog photoelectric signals generated by the CCD sensor at a frequency of 8 MHz, and then outputs 12-bit digital signals. The collected photoelectric signal is used to detect the damage to the surface of ancient buildings with the machine vision technology of artificial intelligence (AI). In the test, the DA circuit can adjust the voltage range of two photoelectric analog signals output by CCD to a predetermined range (1.5 V∼2.0 V). In the ADC circuit test, there is no data in the FIFO when there is no input conversion, and the converted data will be stored in the internal FIFO during the conversion clock period. Based on machine vision technology, surface damage types of ancient buildings are defined, namely spalling, cracks, and disruption, and surface image samples are generated from collected signals. The samples are trained using the convolutional neural network, and the classifier is generated. The test reveals that the designed photoelectric signal acquisition device and AI machine vision technology can accurately classify the surface damage of ancient buildings.

Diamond immersion photodetector for 213 nm deep-ultraviolet photodetection

Developing a photoelectric detector with independent power, compactness, and high environmental sensitivity is a key step to realize the energy-saving and sustainable integrated photoelectric system of next generation. In this paper, a p-type diamond single crystal (PDSC) is used as the photoelectrode to construct a self-powered deep ultraviolet light immersion photodetector with the photoelectrochemical (PEC) cell structure. Under the excitation of a deep ultraviolet light of 213 nm, this immersion photodetector exhibits an excellent responsivity of 20 mA W−1 and a short response/recovery time (50/38 ms). This superior performance is attributed to the successful synthesis of high-quality p-type diamond by the high-temperature and high-pressure method, which ensures an efficient light detection through the effective light collection. This kind of immersion detector based on the ultra-wide-bandgap diamond guarantees a stable detection for deep ultraviolet detection with efficient response, which displays a great potential in future application.

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children.

A mimic fusion method based on difference feature association falling shadow for infrared and visible video

Optimal selection of fusion strategy based on image difference information is an important way to improve the adaptive performance of infrared photoelectric detection systems. Heterogeneous difference feature amplitudes often have different dimensions and orders of magnitude, so the traditional fusion model cannot adjust the fusion algorithm effectively according to the difference feature attribute value, resulting in poor fusion effect or even fusion failure. Furthermore, the existing fusion methods often ignore the association between the different feature information, which leads to problems such as weak semantic interpretation of the fusion process and difficulty in improving the fusion effect. As a result, we draw on the multi-mimetic idea of mimic octopus, and propose a mimic fusion method based on difference feature association falling shadows for infrared and visible video. Firstly, we select six difference features to quantitatively describe the amplitudes of three types of significant complementary information in bimodal video. Secondly, the difference feature frequency distribution is obtained by KNNE. Thirdly, construct the difference feature comprehensive weight to coordinate the relationship between the multiple attributes of difference feature, so as to determine the main difference feature of each frame according to the result. Then use PCCs to calculate the correlation between the any two difference features comprehensive weights to obtain the feature association matrix. Finally, the association synthesis rules of the heterogeneous difference feature fusion validity are established, and the mimic algorithm variables are optimized and selected according to the association falling shadow results to realize the mimic fusion for infrared and visible video. The experimental results show that the method in this paper has achieved remarkable results in preserving the typical infrared target and visible light structural details in the whole video, and is significantly better than other single fusion methods in quantitative analysis and qualitative evaluation.

Integration Optimization of Integrated Solar Combined Cycle (ISCC) System Based on System/Solar Photoelectric Efficiency

Integrated solar combined cycle (ISCC) systems play a pivotal role in the utilization of non-fossil energy; however, the efficient application of solar energy has emerged as a primary issue in the study of ISCC systems. Therefore, it is extremely urgent to propose the best optimization scheme for ISCC under different operating conditions. In this paper, according to the idea of temperature matching and cascade utilization, the optimization of the ISCC system is carried out with the genetic algorithm for the whole working conditions, and the optimization schemes with the highest photoelectric efficiency and system efficiency under different working conditions are derived. In comparison with two optimization schemes with different objective functions, the conclusion can be drawn that: At 100% gas turbine load—30% DNI and 100% gas turbine load—100% DNI working conditions, respectively, the maximum system efficiency of 56.32% and the maximum solar photoelectric efficiency of 35.5% are attained. With the decreasing of gas turbine load, the solar energy integration position will gradually change from the topping cycle to the bottom cycle; with the gas turbine load variation from 100% to 75%, the optimal photoelectric efficiency model prefers two-stage integration, and up to 141.3 MW of solar energy could be integrated, which is greater than the maximum value of 127.1 MW for the optimal system efficiency model. Regarding the heat collection choice of bottom cycle, the optimal photoelectric efficiency model prefers the high-pressure boiler (HPB), while the optimal system efficiency model prefers the high-pressure superheater (HPS). The comparison between the optimal solution and the actual cases confirms the correctness of the optimization results and provides guidance for the subsequent ISCC study.

Inference-Optimized High-Performance Photoelectric Target Detection Based on GPU Framework

Deep learning has better detection efficiency than typical methods in photoelectric target detection. However, classical CNNs on GPU frameworks consume too much computing power and memory resources. We propose a multi-stream inference-optimized TensorRT (MSIOT) method to solve this problem effectively. MSIOT uses knowledge distillation to effectively reduce the number of model parameters by layer guidance between CNNs and lightweight networks. Moreover, we use the TensorRT and multi-stream mode to reduce the number of model computations. MSIOT again increases inference speed by 9.3% based on the 4.3–7.2× acceleration of TensorRT. The experimental results show that the model’s mean average accuracy, precision, recall, and F1 score after distillation can reach up to 94.20%, 93.16%, 95.4%, and 94.27%, respectively. It is of great significance for designing a real-time photoelectric target detection system.

A novel continuous all-weather photo-electric synergistic treatment system for refractory organic compounds and its application in degrading enrofloxacin

A novel continuous all-weather photo-electric synergistic treatment system was proposed in this study for refractory organic compounds, which overcame the defects of conventional photo-catalytic treatments that rely on light irradiation and thus cannot achieve all-weather continuous treatment. The system used a new photocatalyst (MoS2/WO3/carbon felt) with the characteristics of easy recovery and fast charge transfer. The system was systematically tested in degrading enrofloxacin (EFA) under real environmental conditions in terms of treatment performance, pathways and mechanisms. The results showed that the EFA removal of photo-electric synergy substantially increased by 1.28 and 6.78 times, compared to photocatalysis and electrooxidation, respectively, with an average removal of 50.9% under the treatment load of 832.48 mg m−2 d−1. Possible treatment pathways of EFA and mechanism of the system were found to be mainly the loss of piperazine groups, the cleavage of the quinolone portion and the promotion of electron transfer by bias voltage.

Ultrabroadband Imaging Based on Wafer-Scale Tellurene.

High-resolution imaging is at the heart of the revolutionary breakthroughs of intelligent technologies, and it is established as an important approach toward high-sensitivity information extraction/storage. However, due to the incompatibility between non-silicon optoelectronic materials and traditional integrated circuits as well as the lack of competent photosensitive semiconductors in the infrared region, the development of ultrabroadband imaging is severely impeded. Herein, the monolithic integration of wafer-scale tellurene photoelectric functional units by exploiting room-temperature pulsed-laser deposition is realized. Taking advantage of the surface plasmon polaritons of tellurene, which results in the thermal perturbation promoted exciton separation, in situ formation of out-of-plane homojunction and negative expansion promoted carrier transport, as well as the band bending promoted electron-hole pair separation enabled by the unique interconnected nanostrip morphology, the tellurene photodetectors demonstrate wide-spectrum photoresponse from 370.6 to 2240nm and unprecedented photosensitivity with the optimized responsivity, external quantum efficiency and detectivity of 2.7×107 AW-1 , 8.2×109 % and 4.5 × 1015 Jones. An ultrabroadband imager is demonstrated and high-resolution photoelectric imaging is realized. The proof-of-concept wafer-scale tellurene-based ultrabroadband photoelectric imaging system depicts a fascinating paradigm for the development of an advanced 2D imaging platform toward next-generation intelligent equipment.

Numerical Simulation of Integrated Generation and Shaping of Airy and Bessel Vortex Beams Based on All-Dielectric Metasurface

Integrating multiple independent functions into a single optical component is one of the most important topics in research on photoelectric systems. In this paper, we propose a multifunctional all-dielectric metasurface that can achieve a variety of non-diffractive beams depending on the polarization state of the incident light. Using the anisotropic TiO2 rectangular column as the unit structure, the three functions of generating polygonal Bessel vortex beams under left-handed circularly polarized incidence, Airy vortex beams under right-handed circularly polarized incidence and polygonal Airy vortex-like beams under linearly polarized incidence are realized. In addition, the number of polygonal beam sides and the position of focal plane can be adjusted. The device could facilitate further developments in scaling complex integrated optical systems and fabricating efficient multifunctional components.

A Tunable Single-Molecule Light-Emitting Diode with Single-Photon Precision.

A robust single-molecule light-emitting diode (SM-LED) with high color purity, linear polarization, and efficiency tunability is prepared by covalently integrating one fluorescent molecule into nanogapped graphene electrodes. Furthermore, single-molecule Förster resonance energy transfer from the electroluminescent center to different accepters is achieved through rational molecular engineering, enabling construction of a multicolor SM-LED. All these characterizations are accomplished in the photoelectrical integration system with high temporal/spatial/energy resolution, demonstrating the capability of the single-photon emission of SM-LEDs. The success in developing high-performance SM-LEDs inspires the development of the next generation of commercial display devices and promises a single-photon emitter for use in quantum computation and quantum communication.

A novel smart photoelectric lock system: Speech transmitted by laser and speech to text

We propose a circuit that modulates a speech signal to a laser, using which the speech signal can be transmitted using the laser. Also, it shows the use of a platform based on embedded ARM (Advanced RISC Machine), running a small deep learning model based on TDNN (Time delay neural network) and LSTM (Long short-term memory), and converting speech to text, and use the text cipher for unlocking. This research implements a smart lock system that can set a pre-record speech cipher and verify the similarity through a laser transmission speech cipher to unlock it. In our experiment result, the English speech of laser transmission can reach a WER (Word error rate) of 14.06% through the deep learning model to recognize the content of the speech cipher. We also design a similarity comparison algorithm based on LCS (Longest common subsequence) to compare the character set of the laser transmission speech compare and the prerecord speech cipher to calculate the similarity rate. Through the similarity comparison algorithm, when the WER is 27.27%, the male speech samples used in this study still have a 95% unlocking success rate, while the female speech samples have a 100% unlocking success rate. Compared with only using automatic speech recognition (ASR) to unlock, the method we propose is to compare the similarity of the content of speech cipher. The method significantly improves the unlocking fault tolerance of using lasers to transmit audio. Therefore, by using the laser to transmit the speech cipher, the usability of the photoelectric smart lock system has been significantly improved. At the same time, the characteristics of the laser are not easy to eavesdrop on the cipher, which can also improve security.

Hybrid Nonsingleton Fuzzy Strong Tracking Kalman Filtering for High Precision Photoelectric Tracking System

This article presents a hybrid nonsingleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high-precision photoelectric tracking system (PTS) to improve state estimation performance in the complex case of measurement noise change and velocity state mutation. The proposed H-NFSTKF is composed of three parts: A strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a nonsingleton FLS (NFLS). To compare different nonsingleton firing strength approaches, standard method (Sta-NS), centroid-based method (Cen-NS), similarity-based method (Sim-NS), and subsethood-based method (Sub-NS) are discussed. In addition, two traditional control strategies, i.e, dual closed-loop and feed-forward control, as well as four filtering methods including Kalman filter (KF), STKF, fuzzy STKF (FSTKF), and single nonsingleton FSTKF (S-NFSTKF) are also designed to show the superiority of the proposed H-NFSTKF. Finally, by means of comparative simulation analyses and experimental results, the excellence of the proposed H-NFSTKF is verified.

Advances in silicon-based in-sensor computing for neuromorphic vision sensors

With the development of the Internet of Things (IoT), intelligent security systems, assisted driving and artificial intelligence, traditional machine vision sensors no longer meet the needs of low power consumption, high sampling rate, high dynamic range and low data redundancy, therefore, the emergence of new vision sensors is urgently needed. Drawing on the information extraction and encoding methods of biological retinas, in-sensor computing photoelectric detection systems have been developed. It has a more efficient sampling model and data transfer mode than frame-based and separated sensory-computing systems. This paper provides an overview of several major silicon-based in-sensor computing neuromorphic vision sensors. Using bio-vision mechanisms as an entry point, the architecture and working principles of these neuromorphic optoelectronic sensors are focused on, their application scenarios, advantages and limitations are compared, and the challenges and possible directions for development are analysed.

Limit detection sensitivity analysis and maximum detection height prediction for the photoelectric detection target

Photoelectric detection target (PDT) is a kind of non-contact photoelectric detection systems widely used in high-velocity projectile testing for ballistic trajectory measurements. However, invalid measurement data are often acquired during the experiments due to the flying heights of a measured object exceeding its maximum detectable height which is determined by limit detection sensitivity (LDS). As quantitative LDS is difficult to be analyzed and hence the maximum detectable height is usually estimated empirically. Here, we present a model to evaluate the LDS and the maximum detection height in a PDT to ensure the reliability of measurement results. With the model, the maximum detection height of a PDT can be quantitatively predicted for the first time and the LDS can also be analyzed. Experiments are conducted and the predicted maximum detection heights are in good agreement with the experimental results, which verify the effectiveness of the presented model. The proposed method can be used for calculating the LDS and predicting the maximum detection height exactly that ensuring the validity of measurement results.

Evaluation of a Real-Time Monitoring and Management System of Soybean Precision Seed Metering Devices

Aiming at precise evaluation of the performance of soybean seed metering devices, a photoelectric sensor-based real-time monitoring system was designed. The proposed system mainly included a photoelectric sensor module for seeding signal collecting, Hall sensors speeding module, microcontroller unit (MCU), light and sound alarm module, human–machine interface (HMI), and other parts. The indexes of miss, multiples, flow rate, and application rate were estimated on the basis of seeder speed, seed metering disk rotation rate, photoelectric sensor signals, and clock signals. These real-time statistics of the seeding process were recorded by seeding management system. The laboratory results showed that the detection errors of seeding quantity of both big- and small-diameter soybeans were less than 2.0%. Miss and multiples index estimated by this system were 0.4% and 0.5% than that of seeding image monitoring platform (SIMP), respectively. In field tests, miss and multiples index can be used to evaluate the performance of seed metering device, and big-diameter seeds can be detected more precisely than small ones by these photoelectric sensors. This system can provide support for evaluation of working performance of seed metering devices and have a positive effect on seeding monitoring technology.

Robust and accurate photoelectric system model design using the small-signal principle

Modellemenin önemi ve çeşitli uygulamalarda acil ihtiyaç duyulması nedeniyle, çeşitli çalışmaları doğru bir şekilde yürütmek için gerçek sistemle uyumlu ve simüle eden doğru bir modelin tasarımı, araştırmacılar için büyük bir zorluk haline gelmiştir. Bu nedenle, bu araştırma, yükü DC-DC dönüştürücü aracılığıyla besleyen fotovoltaik panel KC200GT'den oluşan fotovoltaik sistemin küçük sinyal yöntemini kullanarak matematiksel bir modelin tasarımını önermektedir. Bu modelleme yöntemi, DC-DC dönüştürücünün matematiksel modelini doğrusal olmayan davranıştan durum uzayı formülünde verilen doğrusal davranışa dönüştürür, ardından transfer fonksiyonunu elde etmek için Laplace dönüşümünün uygulanması kullanılır. İkincisi, MPP denetleyicisini tasarlamak için çalışmalarda kullanılır. Simülasyon sonuçları, küçük sinyal yöntemine dayalı modelleme yönteminin, gerçek sistemle uyumlu, verimli ve doğru bir model elde etmemizi sağladığını ve bu uyumluluğu aralarındaki voltaj ve enerjide gösterdiğini göstermiştir.